{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -5到5的数据\n",
    "x = torch.linspace(-5,5, 200)\n",
    "x = Variable(x)\n",
    "x_np = x.data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zdwxx\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\torch\\nn\\functional.py:1569: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n",
      "C:\\Users\\zdwxx\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\torch\\nn\\functional.py:1558: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
     ]
    }
   ],
   "source": [
    "y_relu = F.relu(x).data.numpy()\n",
    "y_sigmoid = F.sigmoid(x).data.numpy()\n",
    "y_tanh = F.tanh(x).data.numpy()\n",
    "y_softplus = F.softplus(x).data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YYAMYPRp+9KPcqaTi4YhYUr354APo0gV69YI99oB58yxh6fMsYkn1YuZMaNMmXdRw6aUwbhxsvXXuVFLxsYglFVRtLVx+Oey9d/rxlClwzjnQyL9tpJVyjVhSwfzjH3DssTB+PBx5JAweDBtvnDuVVNwsYkkFMXZsKuGFC1MB9+rlCVnS6nCySFKdLF0Kv/gFHHwwbLEFzJ4NJ55oCUuryxGxpLX2//5feip61izo0weuugq+8pXcqaTSYhFLWiv33gsnnwxVVfDII/DTn+ZOJJUmp6YlrZGPPoKePdOFDd/7HtTUWMJSXVjEklZbTQ1UV8Odd8IvfwlPPw3bbZc7lVTaLGJJqxQjXHddOit64UKYMAEuvBDWcXFLqjP/GEn6Uu+9l64sfPxx6NABbr8dmjXLnUoqH46IJX2hp5+G1q3THuFBg2DECEtYKjSLWNJ/Wb4czj8f2reH9deHGTPS7UnuDZYKz6lpSf/hjTega1eYOhV69IAbboCvfjV3Kql8WcSS/s9jj8EJJ8CyZXD33WmLkqT65dS0JD75BPr2TfuBv/51eO45S1hqKBaxVOH+9Ke0Lemmm+D002HaNPjmN3OnkiqHU9NShYoRhg5ND2F99aswejT86Ee5U0mVpyAj4hDCwSGEl0IIr4QQzi7Ea0qqPx98AJ07p1uS9toL5s2zhKVc6lzEIYQq4EbgR8DOQJcQws51fV1J9WPGjLQ3+JFH4LLL0h7hrbfOnUqqXIUYEbcFXokxvhpjXArcDxxagNeVVEC1tfCb38Dee6efT50KZ58NjXxSRMqqEH8EmwNvfObnb376a5KKxN//DgcdBOecA506paei27XLnUoSFKaIV3bWTvyvbwqhdwhhdghh9vz58wvwtpJWx5gx0KoVPPMM3HYb3H8/bLxx7lSS/lchivhNoMVnfr4t8PbnvynGODjGWB1jrN58880L8LaSvszSpXDGGekhrC23hNmzoVcvj6mUik0hingWsEMIYfsQwrpAZ2BEAV5X0lp65ZX0NPRVV8Epp8Czz8LOPkIpFaU67yOOMS4PIZwKjAWqgGExxhfqnEzSWrnnHjj55HRX8KOPwuGH504k6csU5ECPGONoYHQhXkvS2vnoIzj1VLjzzvRk9D33wNe+ljuVpFVx44JUBp57DnbdFe66K11fOHGiJSyVCotYKmExwqBBaSvSRx/BU0/BBRekaWlJpcE/rlKJeu896NkTRo6EQw6BYcOgWbPcqSStKUfEUgl6+um0N3jcuDQiHj7cEpZKlUUslZDly9MacPv26cakGTPS7UnuDZZKl1PTUon429+ga9d0QtZxx8H116cyllTaLGKpBDz6KJxwAqxYkbYlde2aO5GkQnFqWipin3ySTsbq1Am++c20TckSlsqLRSwVqT/9CXbfHW6+OZ0Z/cwz8I1v5E4lqdCcmpaKTIwwZAicdlpaA37iCTj44NypJNUXR8RSEfnXv+Doo6F373Rpw/PPW8JSubOIpSIxfTq0bg2PPQa/+Q2MHQtbbZU7laT6ZhFLmdXWwmWXwfe/n/YDT5kCZ50FjfzTKVUE14iljP7+d+jeHSZMgKOOgltvhY03zp1KUkOyiKVMnngCevRIlzUMGQLHH+8JWVIlcvJLamBLl8Lpp8OPfwxbbw1z5qTDOixhqTI5IpYa0CuvQOfOqXz79oUrr4QmTXKnkpSTRSw1kLvvhj59oHHjdGTl4YfnTiSpGDg1LdWzjz5Ka8Hdu6ftSTU1lrCkf7OIpXo0dy7ssksaDZ9/PkycCF/7Wu5UkoqJRSzVgxhh0CDYYw9YtAieegouuADWcTFI0uf414JUYPPnQ8+eMGoUHHIIDBsGzZrlTiWpWDkilgpo4kRo1QrGj4frroPhwy1hSV/OIpYKYPly+OUvYb/9oGlTmDkTfvYz9wZLWjWnpqU6+utf4Zhj0n3BPXvC9dfDBhvkTiWpVFjEUh08+mg6FWvFCrj3XujSJXciSaXGqWlpLXzySTqco1Mn2GEHeO45S1jS2rGIpTX0wgvQti3ccgv84hcwdSp84xu5U0kqVU5NS6spRrjtNujfHzbcEMaMgYMOyp1KUqlzRCythn/9C44+Gk46CfbeG+bNs4QlFYZFLK3C9OnpjOjHHoPLL08j4a22yp1KUrmwiKUvsGIFXHopfP/70KhRWgs+88z0Y0kqFNeIpZV4+2049liYMCFNSd96K2y0Ue5UksqRRSx9zujR6drCjz+GIUPg+OM9IUtS/XGSTfrUkiUwYAD85CewzTYwZ046rMMSllSfHBFLwF/+Ap07p/uD+/aFK6+EJk1yp5JUCSxiVby7706nZDVunJ6MPuyw3IkkVRKnplWxFi5Ma8Hdu0ObNmlvsCUsqaFZxKpIc+fCrrum0fCvfgVPPQUtWuROJakSWcSqKDHCtddCu3awaFEq4F//GtZxkUZSJv71o4oxf366L3jUKOjYEYYNg802y51KUqVzRKyKMHEitGoF48fD9dfD739vCUsqDhaxytry5XDeebDfftC0KTz7LJx6qnuDJRUPp6ZVtv76V+jaFaZNS6djXXcdbLBB7lSS9J8sYpWlRx6BXr3SxQ333gtduuROJEkr59S0ysonn8DJJ8MRR8AOO8Bzz1nCkoqbRayy8cILsNtu6aakM89M1xZ+4xu5U0nSl3NqWiUvRhg8GPr3Tw9kjRkDBx2UO5UkrR5HxCppCxbAUUel6ejvfz8dU2kJSyolFrFK1rRp0Lp12hN8+eVpJLzVVrlTSdKasYhVclasgEsugR/8AKqq0lrwmWdCI383SypBdfqrK4RwRQjhzyGE50MIj4UQNi5UMGll3n4bDjggHdJx5JHpqejdd8+dSpLWXl3HEOOB78QYvwe8DJxT90jSyo0alY6pnDkThg5N+4M32ih3KkmqmzoVcYxxXIxx+ac/nQFsW/dI0n9asgQGDIAOHWCbbWD27HRSlsdUSioHhVxVOx544ou+GELoHUKYHUKYPX/+/AK+rcrZX/4Ce+4J11yTzoieORP+539yp5KkwlnlPuIQwpPAyp5FPTfGOPzT7zkXWA7c80WvE2McDAwGqK6ujmuVVhXld7+DU06BdddNT0YfemjuRJJUeKss4hjj/l/29RBCD6ADsF+M0YJVnS1cCH37piL+wQ/g7ruhRYvcqSSpftT1qemDgbOAjjHGRYWJpEo2Zw7ssgvccw/8+tfw1FOWsKTyVtc14huADYHxIYSaEMItBcikChRjWgfeYw9YvBgmToRf/SrtE5akclans6ZjjN8sVBBVrvnz4bjjYPTotA48dChstlnuVJLUMDyLSFk99VTaGzxhAtxwAzz2mCUsqbJYxMpi2TI491zYf/90KMfMmekBLfcGS6o0XoOoBvf669C1K0yfDiecAIMGwQYb5E4lSXlYxGpQDz8MvXpBbS3cdx907pw7kSTl5dS0GsSiRXDSSemihh13hJoaS1iSwCJWA/jjH6FtWxg8OF1XOHUqfP3ruVNJUnGwiFVvYoRbboHddoP33oOxY+Hyy6Fx49zJJKl4WMSqFwsWpGnoPn3SMZXz5sGBB+ZOJUnFxyJWwT3zDLRuDcOHw29/C088AVtumTuVJBUni1gFs2IFXHIJ7LMPrLNOKuRf/AIa+btMkr6Q25dUEG+/Dd26pTOiu3RJa8NNm+ZOJUnFzyJWnY0alc6KXrQIhg1LP/aELElaPU4aaq0tWQI//zl06ADNm6crDHv2tIQlaU04ItZaefnldCDHc8/Bz36WHspq0iR3KkkqPRax1thdd8Epp8B666Unozt2zJ1IkkqXU9NabQsXQvfu0KMH7Lpr2htsCUtS3VjEWi1z5sAuu8C998IFF6R7hLfdNncqSSp9FrG+VG0tXH017LEHLF4MTz8N558PVVW5k0lSeXCNWF/o3XfTVqQnnoDDDoOhQ2HTTXOnkqTy4ohYKzVhArRqlaagb7gBHn3UEpak+mAR6z8sWwYDB8IBB8DGG8PMmdC3r3uDJam+ODWt//P66+l4yhkz4IQTYNAg2GCD3KkkqbxZxALgoYfgxBPTHcL33w9HH507kSRVBqemK9yiRdC7Nxx1FOy0E9TUWMKS1JAs4gr2xz/CbrvBbbfBWWfBlCmw/fa5U0lSZXFqugLFCLfemi5s2GgjGDcuPZwlSWp4jogrzIIFcMQR0KcP7LNPOqbSEpakfCziCvLMM9C6NYwYAVdcAaNHw5Zb5k4lSZXNIq4AK1bAxRenEfA668C0aXDGGdDIf/uSlJ1rxGXurbegW7d0RnTXrnDzzdC0ae5UkqT/ZRGXsZEj01nRn3wCt9+eri/0hCxJKi5OTpahJUugf3845BBo0QLmzk2FbAlLUvGxiMvMyy+nKwsHDYJ+/WD6dNhxx9ypJElfxKnpMhEj3HVXuqChSZP0ZPQhh+ROJUlaFUfEZWDhQujePU0/V1envcGWsCSVBou4xM2eDW3awH33wQUXpHuEmzfPnUqStLos4hJVWwtXXQV77glLl6btSeefD1VVuZNJktaEa8Ql6N1301akMWPgsMNg6FDYdNPcqSRJa8MRcYl58klo1QomToQbb4RHH7WEJamUWcQlYtkyOOccOPBA2GQTePZZOOUU9wZLUqlzaroEvP46dOkCM2ZAr15w7bWwwQa5U0mSCsEiLnIPPQQnnpj2Cd9/Pxx9dO5EkqRCcmq6SC1aBL17w1FHwU47QU2NJSxJ5cgiLkJ/+EM6mGPIEDj7bJgyBbbfPncqSVJ9sIiLSIzpmsLddoMFC2DsWLjsMmjcOHcySVJ9sYiLxPvvQ6dO6UnoH/4wHVN5wAG5U0mS6ptFXASmToXWreHxx+HKK2HUKNhii9ypJEkNwSLOaMUKuOgi2GcfWHddmDYNTj8dGvlvRZIqhtuXMnnrLejWLZ0R3bVrWhtu2jR3KklSQ7OIM3j8cejZExYvhjvugGOP9YQsSapUBZkEDSGcEUKIIYRmhXi9crVkCZx2GnTsCC1awJw56fIGS1iSKlediziE0AI4APhb3eOUr5degnbt4LrroF+/dFzljjvmTiVJyq0QI+JrgDOBWIDXKjsxpunnXXeFN96AESNg0CBYb73cySRJxaBORRxC6Ai8FWOctxrf2zuEMDuEMHv+/Pl1eduS8eGH6YGsnj3TSVnz5sEhh+ROJUkqJqt8WCuE8CSw1Uq+dC4wEDhwdd4oxjgYGAxQXV1d9qPnWbPSjUmvvQYXXggDB0JVVe5UkqRis8oijjHuv7JfDyF8F9gemBfS00bbAnNDCG1jjP8oaMoSUlsLV1+d7g7eemuYNAn23jt3KklSsVrr7Usxxj8A/3f+UwjhdaA6xvheAXKVpHffTU9BjxkDhx+eLm3YdNPcqSRJxcwznArkySehVSuYOBFuugkeecQSliStWsGKOMbYshJHw8uWpWnoAw+ETTZJa8N9+rg3WJK0ejxZqw5eey09kDVzJpx4Ilx7Lay/fu5UkqRSYhGvpQcegN6908j3gQfgqKNyJ5IklSLXiNfQokVp9Nu5M+y8M9TUWMKSpLVnEa+B559PB3MMHZrWhSdPhpYtc6eSJJUyi3g1xJiehG7bFhYsgHHj4NJLoXHj3MkkSaXOIl6F99+HTp2gb19o3z4dU7n/So84kSRpzVnEX2LKFGjdGkaOhKuuSv/cYotV//8kSVpdFvFKrFiRzofed19Yd12YNg0GDIBGflqSpAJz+9LnvPlmujFp0iQ45pi0Nty0ae5UkqRyZRF/xogR6crCJUvSHcLHHusJWZKk+uVkK7B4MfTrB4ceCtttB3PnpssbLGFJUn2r+CJ+6SXYYw+4/no47TSYPh2+9a3cqSRJlaJip6ZjTNPPp54KX/kKPP44dOiQO5UkqdJU5Ij4ww/TA1nHH58O6Zg3zxKWJOVRcUU8axa0aZMuarjoonSPcPPmuVNJkipVxRRxbS1ceSXsuScsX562J513HlRV5U4mSapkFbFG/M476SnosWPhpz+FIUNgk01yp5IkqQJGxOPHQ6tWaQR8883w8MOWsCSpeJRtES9bBmefDQceCJttltaGTz7ZvcGSpOJSllPTr70GXbrAzJnQuzdccw2sv37uVJIk/beyK+IHHkjlG7lY9h8AAAOiSURBVAI8+CAceWTuRJIkfbGymZr++GPo1Qs6d4ZvfxtqaixhSVLxK4sifv55qK6GYcNg4MD0YFbLlrlTSZK0aiU/NX377dCnT3oSevx42G+/3IkkSVp9JT8i3nRTaN8+HVNpCUuSSk3Jj4gPPRQ6dnRbkiSpNJX8iBgsYUlS6SqLIpYkqVRZxJIkZWQRS5KUkUUsSVJGFrEkSRlZxJIkZWQRS5KUkUUsSVJGFrEkSRlZxJIkZWQRS5KUkUUsSVJGIcbY8G8awnzgrw3+xnk1A97LHaLE+RnWnZ9h3fkZ1l0lfobbxRg3X9kXshRxJQohzI4xVufOUcr8DOvOz7Du/Azrzs/wPzk1LUlSRhaxJEkZWcQNZ3DuAGXAz7Du/Azrzs+w7vwMP8M1YkmSMnJELElSRhZxBiGEM0IIMYTQLHeWUhNCuCKE8OcQwvMhhMdCCBvnzlQqQggHhxBeCiG8EkI4O3eeUhNCaBFCmBhCeDGE8EII4bTcmUpVCKEqhPBcCGFk7izFwCJuYCGEFsABwN9yZylR44HvxBi/B7wMnJM5T0kIIVQBNwI/AnYGuoQQds6bquQsB06PMf4P0A7o62e41k4DXswdolhYxA3vGuBMwMX5tRBjHBdjXP7pT2cA2+bMU0LaAq/EGF+NMS4F7gcOzZyppMQY/x5jnPvpjxeSiqR53lSlJ4SwLfATYEjuLMXCIm5AIYSOwFsxxnm5s5SJ44EncocoEc2BNz7z8zexRNZaCKEl0AaYmTdJSbqWNBipzR2kWKyTO0C5CSE8CWy1ki+dCwwEDmzYRKXnyz7DGOPwT7/nXNJU4T0Nma2EhZX8mrMyayGE8FXgEaB/jPHD3HlKSQihA/BujHFOCGHf3HmKhUVcYDHG/Vf26yGE7wLbA/NCCJCmVOeGENrGGP/RgBGL3hd9hv8rhNAD6ADsF91/t7reBFp85ufbAm9nylKyQgiNSSV8T4zx0dx5StBeQMcQwo+BJkDTEMLdMcZumXNl5T7iTEIIrwPVMcZKO/i8TkIIBwNXA/vEGOfnzlMqQgjrkB5u2w94C5gFdI0xvpA1WAkJ6b+g7wTejzH2z52n1H06Ij4jxtghd5bcXCNWqbkB2BAYH0KoCSHckjtQKfj0AbdTgbGkh4wetITX2F5Ad6D9p7/3aj4d2Ul14ohYkqSMHBFLkpSRRSxJUkYWsSRJGVnEkiRlZBFLkpSRRSxJUkYWsSRJGVnEkiRl9P8BtbMEaQIWTrgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HDkwp/S0ixgOTgX077T84pfRcubVIqg3Tp8O228J+++VdiVT/KtEj3wdYkFJ6LKX0OnA1MLFzg5TSnSmlv5VW7wKGVuBzJdWg1auzHvlhh2X3kEvqW5UI8iHA053WF5a2dedzwC2d1hMwMyLmRsSkCtQjKUdz58KyZTBhQt6VSI2h7KF1oKsHE6YuG0YcTBbkH+60eXRKaVFEbA/Mioi/pJRu6+LYScAkgOHDh5dftaQ+MX169rjSww7LuxKpMVSiR74QGNZpfSiwaO1GEbEbcAkwMaX0/JrtKaVFpdelwHVkQ/Vvk1KanFJqTik1Dxo0qAJlS+oL06fDvvvCdtvlXYnUGCoR5HOAERGxS0T0B44FWjo3iIjhwLXASSmlv3bavnlEDFzzHhgLPFSBmiTlYNmy7EEpDqtL1VP20HpKqT0iTgdagSZgSkppXkScWtp/EfAtYFvglxEB0J5SagZ2AK4rbesHXJVSmlFuTZLy0doKKfmQFKmaIqUuT2fXtObm5tTW5i3nUq05/niYPRsWL4aNnG5KqpiImFvqAL+Nv2qSKqKjI7vtbPx4Q1yqJn/dJFXE3XfD3/7m+XGp2gxySRVxyy3ZBDBjxuRdidRYDHJJFTF9Ouy/P2y9dd6VSI3FIJdUtsWL4Z57vFpdyoNBLqlsM0o3jXp+XKo+g1xS2VpbYfBg2G23vCuRGo9BLqksHR0waxaMHZvNsS6pugxySWWZOxdeeMGHpEh5McgllaW1NeuJe9uZlA+DXFJZWlthr7182pmUF4Nc0gZ76SW46y6H1aU8GeSSNtjs2dnFbga5lB+DXNIGa22FgQNhv/3yrkRqXAa5pA2SUhbkhxwCG2+cdzVS4zLIJW2Qv/4VnnzSYXUpbwa5pA3S2pq9GuRSvgxySRuktRVGjIBddsm7EqmxGeSSeu211+CPf8ymZZWUL4NcUq/dcQesWOGwulQLDHJJvTZzZnal+sEH512JJINcUq+1tsLo0bDFFnlXIskgl9Qrzz4L99/vsLpUKwxySb0yc2b2apBLtcEgl9Qrra2w/faw++55VyIJDHJJvbB6ddYjHzsWNvJvD6km+KsoqcfuvReee85hdamWGOSSemzNtKxOBCPVjooEeUSMi4hHI2JBRJzVxf6IiPNL+x+IiD17eqyk2tHaCqNGZefIJdWGsoM8IpqAC4DxwEjguIgYuVaz8cCI0jIJuLAXx0qqAS+/DHfe6bC6VGsq0SPfB1iQUnospfQ6cDUwca02E4HLU+Yu4B0RMbiHx0qqAX/4A7S3G+RSralEkA8Bnu60vrC0rSdtenKspBrQ2gqbbw777593JZI6q0SQRxfbUg/b9OTY7AdETIqItohoW7ZsWS9LlFSumTPhoINgwIC8K5HUWSWCfCEwrNP6UGBRD9v05FgAUkqTU0rNKaXmQYMGlV20pJ577DFYsMBhdakWVSLI5wAjImKXiOgPHAu0rNWmBfh06er1/YCXUkqLe3ispJytue3MIJdqT79yf0BKqT0iTgdagSZgSkppXkScWtp/ETAdmAAsAFYAn13XseXWJKmyZs6EnXaCESPyrkTS2soOcoCU0nSysO687aJO7xNwWk+PlVQ7Vq2C2bPhuOMgurqqRVKunNlN0jrddVd2D7nD6lJtMsglrVNrKzQ1wUc+knclkrpikEtap5kzYd994R3vyLsSSV0xyCV167nnoK3NYXWplhnkkrp1662QkkEu1TKDXFK3Zs6ErbeG5ua8K5HUHYNcUpdSyi50O/TQ7GI3SbXJIJfUpXnzYNEih9WlWmeQS+rSmmlZx47Ntw5J62aQS+rSzJmw664wbNj620rKj0Eu6W1WroTbbnNYXSoCg1zS29x2G7z6qsPqUhEY5JLeZuZMGDAADjww70okrY9BLultWlvhgANgs83yrkTS+hjkkt5i4cLs1jOH1aViMMglvcWsWdmrF7pJxWCQS3qL1lYYPBg++MG8K5HUEwa5pDd0dGQ98rFjISLvaiT1hEEu6Q1z58ILL3h+XCoSg1zSG2bOzHriY8bkXYmknjLIJb1hxgzYYw8YNCjvSiT1lEEuCYC//Q3+7/9gwoS8K5HUGwa5JCAbVl+92iCXisYglwTA9OmwzTawzz55VyKpNwxySaxenZ0fP+wwaGrKuxpJvWGQS+Kee2DpUofVpSIyyCUxfXp225nTskrFY5BL4pZbsnPj3nYmFU9ZQR4R20TErIiYX3rduos2wyLiDxHxSETMi4gvdtp3TkQ8ExH3lRYH9qQqe+45uPtuGD8+70okbYhye+RnAbNTSiOA2aX1tbUDZ6SUdgX2A06LiJGd9v8kpTSqtEwvsx5JvdTaCil5flwqqnKDfCJwWen9ZcDH1m6QUlqcUrqn9P5l4BFgSJmfK6lCpk/PhtT32ivvSiRtiHKDfIeU0mLIAhvYfl2NI2JnYA/g7k6bT4+IByJiSldD85L6TkdH1iMfPx428ooZqZDW+6sbEbdGxENdLBN780ERsQUwDfhSSunvpc0XAu8GRgGLgR+t4/hJEdEWEW3Lli3rzUdL6sacOfD8854fl4qs3/oapJQO7W5fRCyJiMEppcURMRhY2k27jclC/MqU0rWdfvaSTm0uBm5aRx2TgckAzc3NaX11S1q/6dOznriPLZWKq9zBtBbg5NL7k4Eb1m4QEQFcCjySUvrxWvsGd1o9CniozHok9cLNN8N++2VTs0oqpnKD/FxgTETMB8aU1omIHSNizRXoo4GTgI90cZvZDyLiwYh4ADgY+HKZ9UjqoWeeyWZ0O+KIvCuRVI71Dq2vS0rpeeCQLrYvAiaU3t8BRDfHn1TO50vacDeVTmQdeWS+dUgqj9epSg2qpQXe/W7Ydde8K5FUDoNcakDLl8Ps2VlvPLocL5NUFAa51IBmzYLXXnNYXaoHBrnUgFpaYOutYfTovCuRVC6DXGowHR3ZhW7jx8PGG+ddjaRyGeRSg7n77uyJZw6rS/XBIJcaTEsL9OsH48blXYmkSjDIpQbT0gIHHQRbbZV3JZIqwSCXGsj8+fDIIw6rS/XEIJcayI03Zq9OyyrVD4NcaiAtLbDbbrDzznlXIqlSDHKpQSxbBrff7rC6VG8McqlBXH89rF4NxxyTdyWSKskglxrE1KnwnvdkQ+uS6odBLjWAF16A//kfOPpoH5Ii1RuDXGoALS3Q3u6wulSPDHKpAUydCjvtBHvtlXclkirNIJfq3EsvZY8tdVhdqk8GuVTnbroJXn/dYXWpXhnkUp2bOhV23BH23TfvSiT1BYNcqmPLl8OMGdmw+kb+tkt1yV9tqY5Nnw6vvuqwulTPDHKpjk2dCttvD6NH512JpL5ikEt16pVX4Oab4aijoKkp72ok9RWDXKpT118PK1bA8cfnXYmkvmSQS3Xqyith2DD48IfzrkRSXzLIpTq0dCnMnAknnODV6lK981dcqkPXXAMdHXDiiXlXIqmvlRXkEbFNRMyKiPml1627afdERDwYEfdFRFtvj5fUO1dcAbvvDu9/f96VSOpr5fbIzwJmp5RGALNL6905OKU0KqXUvIHHS+qB+fPhz3+2Ny41inKDfCJwWen9ZcDHqny8pLVcdVX2cJTjjsu7EknVUG6Q75BSWgxQet2+m3YJmBkRcyNi0gYcL6kHUsqG1Q8+GIYMybsaSdXQb30NIuJW4J1d7PpGLz5ndEppUURsD8yKiL+klG7rxfGU/gEwCWD48OG9OVRqGHPmwIIF8PWv512JpGpZb5CnlA7tbl9ELImIwSmlxRExGFjazc9YVHpdGhHXAfsAtwE9Or507GRgMkBzc3NaX91SI7riChgwIHtIiqTGUO7Qegtwcun9ycANazeIiM0jYuCa98BY4KGeHi+pZ1atym47O/JI2GqrvKuRVC3lBvm5wJiImA+MKa0TETtGxPRSmx2AOyLifuDPwM0ppRnrOl5S7910UzYRzKc/nXclkqopUireKHVzc3Nqa2tbf0OpgUyYAA88AE88Af3We9JMUpFExNy1bt9+gzO7SXXgySdhxgz43OcMcanRGORSHZgyJXv9p3/Ktw5J1WeQSwXX3g6XXgrjxsFOO+VdjaRqM8ilgrvlFnjmGfj85/OuRFIeDHKp4C6+GN75Tjj88LwrkZQHg1wqsIUL4eab4bOfhY03zrsaSXkwyKUC+/WvYfVqOOWUvCuRlBeDXCqojg645BI49FB417vyrkZSXgxyqaBuvhmeegomTVp/W0n1yyCXCur882HoUPjYx/KuRFKeDHKpgB56CGbPhtNO8yI3qdEZ5FIBnX8+bLKJ945LMsilwnn++ey54yeeCNtum3c1kvJmkEsF88tfwsqV8MUv5l2JpFpgkEsFsnJlNqz+0Y/CBz6QdzWSaoFBLhXIf/83PPccnHlm3pVIqhUGuVQQHR1w3nmw775wwAF5VyOpVvTLuwBJPfO738Fjj8EPfwgReVcjqVbYI5cKYPVq+M53YORIJ4CR9Fb2yKUCmDYNHnkEfvtb2Mh/fkvqxL8SpBq3pjf+D/8An/hE3tVIqjX2yKUad+218OCDcPnl0NSUdzWSao09cqmGtbfDN78Ju+4Kxx+fdzWSapE9cqmG/eY38Oij2Tlye+OSumKPXKpRr70G55wDe+8NRx2VdzWSapU9cqlG/eIX8NRTcOml3jcuqXv2yKUatGxZdqX6+PFw6KF5VyOplhnkUg065xxYvhx+9KO8K5FU68oK8ojYJiJmRcT80uvWXbT5h4i4r9Py94j4UmnfORHxTKd9E8qpR6oH8+bBr34Fp56aXa0uSetSbo/8LGB2SmkEMLu0/hYppUdTSqNSSqOAvYAVwHWdmvxkzf6U0vQy65EKLSU47TTYcsusVy5J61NukE8ELiu9vwxY3yzQhwD/L6X0ZJmfK9Wlq66CP/0Jzj0Xttsu72okFUG5Qb5DSmkxQOl1+/W0Pxb47VrbTo+IByJiSldD81KjePFFOOMM2GcfOOWUvKuRVBTrDfKIuDUiHupimdibD4qI/sCRwO87bb4QeDcwClgMdHtpT0RMioi2iGhbtmxZbz5aKoQzz8yuVr/wQh+MIqnn1nsfeUqp25tfImJJRAxOKS2OiMHA0nX8qPHAPSmlJZ1+9hvvI+Ji4KZ11DEZmAzQ3Nyc1le3VCS33goXXwxf/SrsuWfe1UgqknL/3d8CnFx6fzJwwzraHsdaw+ql8F/jKOChMuuRCmf5cvj852HECPiP/8i7GklFU+7MbucCv4uIzwFPAZ8AiIgdgUtSShNK65sBY4B/Xuv4H0TEKCABT3SxX6p7X/kKPPkk3HYbbLpp3tVIKpqygjyl9DzZlehrb18ETOi0vgLYtot2J5Xz+VLR3XBDds/4mWfChz+cdzWSishLaqScLF4Mn/sc7LFHNh2rJG0Ig1zKQXs7HHccrFgBV14J/fvnXZGkovLpZ1IOvvnNbOKXyy5zGlZJ5bFHLlXZ9dfDf/0XTJoEn/503tVIKjqDXKqi+++HE0+EvfeGn/0s72ok1QODXKqSJUvgyCPhHe/IeuWbbJJ3RZLqgefIpSpYvhwOPzybgvX222HHHfOuSFK9MMilPvb663D00XDvvXDddbDXXnlXJKmeGORSH2pvz86Jz5wJU6bAEUfkXZGkeuM5cqmPtLfDSSfB738P550Hn/1s3hVJqkcGudQHXn8964lffTWce272nHFJ6gsOrUsVtmIFHHMM3HJLdr/4mWfmXZGkemaQSxW0dClMnAh33w2TJ2ePJ5WkvmSQSxUyb152i9mSJTB1Knz843lXJKkReI5cqoDLLoN99oGVK7M51A1xSdVikEtleOUV+MxnsmXvveGee7JXSaoWg1zaQA8+mIX25ZfD2WfDrbc6Y5uk6jPIpV5auTJ7DOlee8ELL2STvXz729DPK04k5cAgl3ph1iz44Afhe9+DY4+FBx6AQw/NuypJjcwgl3pg/vwsuMeOhYhsGP3yy2H77fOuTFKjM8ildXjySTjlFNh1V7jxRvjWt7Jz44cckndlkpTxrJ7UhUcfhZ/9DC65JOuBn346nHUWvPOdeVcmSW9lkEslq1fDjBlw/vnQ2gr9+2e3lZ19Ngwblnd1ktQ1g1wN769/hSuvhCuugMceg+lgNBcAAAY5SURBVMGDs6vQJ02CHXbIuzpJWjeDXA1pwYLsnPdVV0FbWzZ8fvDB8N3vwtFHZ71xSSoCg1wNYcUKuPNOmD4dbr4564UD7Lkn/OhH2RXpTuYiqYgMctWlZ5+FuXPh9tvhttuyXveqVTBgABx0UHbx2kc/Cu96V96VSlJ5DHIVWnt7dl77vvuy5d57s2XJkmz/xhtn06iecQYccAAceCBsvnm+NUtSJRnkqmkpwYsvwjPPwFNPZee2Oy+PP56FOWRTpL7//TB+PIwaBXvsAc3NsNlm+f4ZJKkvlRXkEfEJ4BxgV2CflFJbN+3GAT8DmoBLUkrnlrZvA1wD7Aw8AXwypfS3cmpS7VuxAp5//u3Lc89lr0uXZsH9zDOwaFE2t3lnW2wBI0ZkYX3MMfDe98Luu8PIkdnQuSQ1knJ75A8BHwd+1V2DiGgCLgDGAAuBORHRklJ6GDgLmJ1SOjciziqtf63MmtRJStn90R0dby7t7W9d72pbezu89lrvlhUr4OWXYfnybOn8vvP6qlXd17vFFjBoEAwZkvWmhwx5cxk6FN7znmxa1IjqfYeSVMvKCvKU0iMAse6/VfcBFqSUHiu1vRqYCDxcej2o1O4y4I9UOcgnT4YpU7L3KWVL5/frW8+jbVf7Oof12qFcLZttBgMHZmG8Ztl662wylS22eHPfllvCdtvBttu+ddlmG3vUktRb1ThHPgR4utP6QmDf0vsdUkqLAVJKiyOi20dQRMQkYBLA8OHDK1bcgAGw1VZv9vAi3v5+feuValvOz4mApqbsPHFT05vL2us9abNmvX9/2GST7Dta17LJJtlFZfaSJan61hvkEXEr0NUM099IKd3Qg8/o6q/31IPj3npASpOByQDNzc29Pr47J5+cLZIkFdF6gzylVO7TlhcCnWeqHgosKr1fEhGDS73xwcDSMj9LkqSGUo3HmM4BRkTELhHRHzgWaCntawHW9IdPBnrSw5ckSSVlBXlEHBURC4EPATdHRGtp+44RMR0gpdQOnA60Ao8Av0spzSv9iHOBMRExn+yq9nPLqUeSpEYTKVXsdHPVNDc3p7a2Lm9ZlySp7kTE3JRSc1f7qjG0LkmS+ohBLklSgRnkkiQVmEEuSVKBGeSSJBWYQS5JUoEZ5JIkFZhBLklSgRnkkiQVWCFndouIZcCTeddRZdsBz+VdRMH5HZbP77B8fofla8TvcKeU0qCudhQyyBtRRLR1Nz2fesbvsHx+h+XzOyyf3+FbObQuSVKBGeSSJBWYQV4ck/MuoA74HZbP77B8fofl8zvsxHPkkiQVmD1ySZIKzCAvoIj4SkSkiNgu71qKJiJ+GBF/iYgHIuK6iHhH3jUVRUSMi4hHI2JBRJyVdz1FExHDIuIPEfFIRMyLiC/mXVNRRURTRNwbETflXUstMMgLJiKGAWOAp/KupaBmAR9IKe0G/BX4es71FEJENAEXAOOBkcBxETEy36oKpx04I6W0K7AfcJrf4Qb7IvBI3kXUCoO8eH4CnAl4ccMGSCnNTCm1l1bvAobmWU+B7AMsSCk9llJ6HbgamJhzTYWSUlqcUrqn9P5lsiAakm9VxRMRQ4GPApfkXUutMMgLJCKOBJ5JKd2fdy114p+AW/IuoiCGAE93Wl+IIbTBImJnYA/g7nwrKaSfknVmVuddSK3ol3cBequIuBV4Zxe7vgH8GzC2uhUVz7q+w5TSDaU23yAb6ryymrUVWHSxzVGhDRARWwDTgC+llP6edz1FEhGHA0tTSnMj4qC866kVBnmNSSkd2tX2iPggsAtwf0RANiR8T0Tsk1J6tool1rzuvsM1IuJk4HDgkOT9lz21EBjWaX0osCinWgorIjYmC/ErU0rX5l1PAY0GjoyICcAmwJYRcUVK6cSc68qV95EXVEQ8ATSnlBrtwQFliYhxwI+BA1NKy/Kupygioh/ZxYGHAM8Ac4DjU0rzci2sQCL7F/hlwAsppS/lXU/RlXrkX0kpHZ53LXnzHLkazS+AgcCsiLgvIi7Ku6AiKF0geDrQSnaR1u8M8V4bDZwEfKT0/959pZ6lVBZ75JIkFZg9ckmSCswglySpwAxySZIKzCCXJKnADHJJkgrMIJckqcAMckmSCswglySpwP4/32hI6Cd5/fkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(8,6))\n",
    "plt.plot(x_np, x_np, c='blue', label='relu')\n",
    "plt.show()\n",
    "plt.figure(1, figsize=(8,6))\n",
    "plt.plot(x_np, y_tanh, c='blue', label='relu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.7 64-bit",
   "language": "python",
   "name": "python37764bita7c2719225b84763be647c75e40e67b2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
